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Archive of posts filed under the Stan category.

Webinar: A Gaussian Process Model for Response Time in Conjoint Surveys

This post is by Eric. This Wednesday, at 11:30 am ET, Elea Feit is stopping by to talk to us about her recent work on Conjoint models fit using GPs. You can register here. Abstract Choice-based conjoint analysis is a widely-used technique for assessing consumer preferences. By observing how customers choose between alternatives with varying […]

Several postdoc, research fellow, and doctoral student positions in Aalto/Helsinki, Finland

This job ad is by Aki Aalto University, University of Helsinki, and Finnish Center for Artificial Intelligence have a great probabilistic modeling community, and we’re looking for several postdocs, research fellows and doctoral students with topics including a lot of Bayesian statistics. I’m looking for a postodc and doctoral student to work on Bayesian workflow […]

Postdoc opportunity on Bayesian prediction for human-computer interfaces! In Stuttgart!

Paul “brms” Buerkner writes: At the Cluster of Excellence SimTech in Stuttgart, Germany, we are currently looking for a fully funded PostDoc (2 years) to work on Bayesian Intent Prediction for Human-Machine Collaboration, among others supervised by me (Paul-Christian Bürkner). The goal of this specific project is to contribute to the development of a new […]

StanConnect 2021 is happening this summer/fall! Topics are Simulation Based Calibration, Ecology, Biomedical, and Cognitive Science and Neuroscience.

Arman Oganisian writes: The Stan Governing Body is excited to announce this year’s StanConnect 2021 lineup! For those who haven’t yet heard, StanConnect is a series of virtual sessions/mini-symposia held throughout the latter half of this year. Each session hosts research talks (and more) on Bayesian inference via Stan in a different field/topic areas. StanConnect […]

The Tampa Bay Rays baseball team is looking to hire a Stan user

Andrew and I have blogged before about job opportunities in baseball for Stan users (e.g., here and here) and here’s a new one. This time it’s the Tampa Bay Rays who are hiring. The job title is “Analyst, Baseball Research & Development” and here are the responsibilities and qualifications: Responsibilities: * Build customized statistical modeling […]

Stan short course in July

Jonah Gabry is teaching a Stan short course! He’s done it before and I’ve heard that it’s excellent. Here’s the information: Dates: Wed Jul 14 – Fri Jul 16 Location: online Learn Bayesian Data Analysis and Stan with Stan Developer Jonah Gabry The course consists of three main themes: Bayesian inference and computation; the Stan […]

Neel Shah: modeling skewed and heavy-tailed data as approximate normals in Stan using the LambertW function

Neel Shah, one of Stan’s Google Summer of Code (GSOC) interns, writes: Over the summer, I will add LambertW transforms to Stan which enable us to model skewed and heavy-tailed data as approximate normals. This post motivates the idea and describes the theory of LambertW × Z random variables. Though the normal distribution is one […]

When MCMC fails: The advice we’re giving is wrong. Here’s what we you should be doing instead. (Hint: it’s all about the folk theorem.)

In applied Bayesian statistics we often use Markov chain Monte Carlo: a family of iterative algorithms that yield approximate draws from the posterior distribution. For example, Stan uses Hamiltonian Monte Carlo. One annoying thing about these iterative algorithms is that they can take awhile, but on the plus side this spins off all sorts of […]

Network of models

Ryan Bernstein shows this demo of a prototype of the network of models visualization in Stan. This is related to the topology of models, an idea that we’ve discussed on occasion and is a key part of statistical workflow that I don’t think is handled well by existing theory or software. What Ryan is doing […]

Short course on football (soccer) analytics using Stan

From Ioannis Ntzoufras, Dimitrios Karlis, and Leonardo Egidi. I haven’t looked at the course myself but I like the idea!

Postdoc position in Bayesian modeling for cancer

Wesley Tansey writes: I’m recruiting a postdoc to join my lab at Memorial Sloan Kettering Cancer Center (tanseyw@mskcc.org). The role overlaps a lot with the interests of people on your blog. We’re specifically looking for people with experience in subset of the following: – Bayesian hierarchical models – Spatial statistical methods (e.g. Gaussian processes, trend […]

Whatever you’re looking for, it’s somewhere in the Stan documentation and you can just google for it.

Someone writes: Do you have link to an example of Zero-inflated poisson and Zero-inflated negbin model using pure stan (not brms, nor rstanarm)? If yes, please share it with me! I had a feeling there was something in the existing documentation already! So I googled *zero inflated Stan*, and . . . yup, it’s the […]

Hierarchical modeling of excess mortality time series

Elliott writes: My boss asks me: For our model to predict excess mortality around the world, we want to calculate a confidence interval around our mean estimate for total global excess deaths. We have real excess deaths for like 60 countries, and are predicting on another 130 or so. we can easily calculate intervals for […]

Webinar: An introduction to Bayesian multilevel modeling with brms

This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms. We will start with a short introduction to multilevel modeling and to […]

Some issues when using MRP to model attitudes on a gun control attitude question on a 1–4 scale

Elliott Morris writes: – I want to run a MRP model predicting 4 categories of response options to a question about gun control (multinomial logit) – I want to control for demographics in the standard hierarchical way (MRP) – I want the coefficients to evolve in a random walk over time, as I have data […]

StanConnect 2021: Call for Session Proposals

Back in February it was decided that this year’s StanCon would be a series of virtual mini-symposia with different organizers instead of a single all-day event. Today the Stan Governing Body (SGB) announced that submissions are now open for anyone to propose organizing a session. Here’s the announcement from the SGB on the Stan forums:  […]

Discuss our new R-hat paper for the journal Bayesian Analysis!

Here’s your opportunity: We welcome public contributions to the Discussion of the manuscript the manuscript Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC by A. Vehtari, A. Gelman, D. Simpson, B. Carpenter and P. C. Bürkner, which will be featured as a Discussion Paper in the June 2021 issue of the […]

The Folk Theorem, revisited

It’s time to review the folk theorem, an old saw on this blog, on the Stan forums, and in all of Andrew’s and my applied modeling. Folk Theorem Andrew uses “folk” in the sense of being folksy as opposed to rigorous. The Folk Theorem of Statistical Computing (Gelman 2008): When you have computational problems, often […]

Work on Stan as part of Google’s Summer of Code!

The Stan project is excited to announce that we will be participating in Google Summer of Code (GSoC) 2021 as a mentoring organization under the NumFOCUS umbrella. GSoC is an initiative that connects students with open source projects to give them hands-on experience working on open source code. We are thrilled to offer three projects […]

PhD student and postdoc positions in Norway for doing Bayesian causal inference using Stan!

Guido Biele writes: I have two positions for a postdoc and PhD student open in a project where we will use observational data from Norwegian National registries, structural models (or the potential outcomes framework, the main thing is that we want to think systematically about identification), and Bayesian estimation in Stan to estimate causal effects […]

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